The cost of passing -- using deep learning AIs to expand our understanding of the ancient game of Go. (arXiv:2208.12643v1 [cs.AI])
AI engines utilizing deep learning neural networks provide excellent tools
for analyzing traditional board games. Here we are interested in gaining new
insights into the ancient game of Go. For that purpose, we need to define new
numerical measures based on the raw output of the engines. In this paper, we
develop a numerical tool for automated move-by-move performance evaluation in a
context-sensitive manner and for recognizing game features. We measure the
urgency of a move by the cost of passing, which is the score value difference
between the current configuration of stones and after a hypothetical pass in
the same board position. Here we investigate the properties of this measure and
describe some applications.